01014813

Component

https://doi.org/10.3141/1917-18

http://scholar.google.com...Baik&publication_year=2005

Vehicle class is an important parameter in the process of road traffic measurement. Inductive loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve their accuracy, a new algorithm is suggested for ILD using backpropagation neural networks. In the developed algorithm, inputs to the neural networks are the variation rate of frequency and occupancy time. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.7%. Results verified that, compared with the conventional method based on ILD, the proposed algorithm improves the vehicle classification accuracy.

01014803

English

pp 164-172

2005

Transportation Research Record: Journal of the Transportation Research Board

0309093902

Print

Figures (8) ; Photos (1) ; References (15) ; Tables (5)

Accuracy; Algorithms; Loop detectors; Neural networks; Vehicle classification

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 27 2005 3:58PM

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